In [1]:
%matplotlib inline
import pandas as pd
import socket
host = socket.getfqdn()

from core import  load, zoom, calc, save,plots,monitor
In [2]:
#reload funcs after updating ./core/*.py
import importlib
importlib.reload(load)
importlib.reload(zoom)
importlib.reload(calc)
importlib.reload(save)
importlib.reload(plots)
importlib.reload(monitor)
Out[2]:
<module 'core.monitor' from '/ccc/work/cont003/gen7420/odakatin/monitor-sedna/notebook/core/monitor.py'>

If you submit the job with job scheduler, above¶

below are list of enviroment variable one can pass

%env local='2"¶

local : if True run dask local cluster, if not true, put number of workers setted in the 'local' if no 'local ' given, local will be setted automatically to 'True'

%env ychunk='2'¶

%env tchunk='2'¶

controls chunk. 'False' sets no modification from original netcdf file's chunk.¶

ychunk=10 will group the original netcdf file to 10 by 10¶

tchunk=1 will chunk the time coordinate one by one¶

%env file_exp=¶

'file_exp': Which 'experiment' name is it?¶

. this corresopnds to intake catalog name without path and .yaml¶

%env year=¶

for Validation, this correspoinds to path/year/month 's year¶

for monitoring, this corresponids to 'date' having * means do all files in the monitoring directory¶

setting it as 0[0-9] &1[0-9]& *[2-3][0-9], the job can be separated in three lots.¶

%env month=¶

for monitoring this corresponds to file path path-XIOS.{month}/¶

#

%env control=FWC_SSH¶

name of control file to be used for computation/plots/save/ & how it is called from Monitor.sh¶

Monitor.sh calls M_MLD_2D

and AWTD.sh, Fluxnet.sh, Siconc.sh, IceClim.sh, FWC_SSH.sh

  • AWTD.sh M_AWTMD

  • Fluxnet.sh M_Fluxnet

  • Siconc.sh M_Ice_quantities
  • IceClim.sh M_IceClim M_IceConce M_IceThick

FWC_SSH.sh M_FWC_2D M_FWC_integrals M_FWC_SSH M_SSH_anomaly

M_Mean_temp_velo

M_Mooring M_Sectionx M_Sectiony

%env save= proceed saving? True or False , Default is setted as True¶

%env plot= proceed plotting? True or False , Default is setted as True¶

%env calc= proceed computation? or just load computed result? True or False , Default is setted as True¶

%env save=False¶

%env lazy=False¶

For debugging this cell can help¶

%env file_exp=SEDNA_DELTA_MONITOR %env year=2012 %env month=01

0[1-2]¶

%env ychunk=10 %env ychunk=False %env save=False %env plot=True %env calc=True # %env lazy=False

False¶

%env control=M_Fluxnet

M_Sectiony ok with ychunk=False local=True lazy=False¶

In [3]:
%%time
# 'savefig': Do we save output in html? or not. keep it true. 
savefig=True
client,cluster,control,catalog_url,month,year,daskreport,outputpath = load.set_control(host)
!mkdir -p $outputpath
!mkdir -p $daskreport
client

local True
using host= irene5809.c-irene.mg1.tgcc.ccc.cea.fr starting dask cluster on local= True
This code is running on  irene5809.c-irene.mg1.tgcc.ccc.cea.fr using  SEDNA_DELTA_MONITOR file experiment, read from  ../lib/SEDNA_DELTA_MONITOR.yaml  on year= 2012  on month= 02  outputpath= ../results/SEDNA_DELTA_MONITOR/ daskreport= ../results/dask/6418604irene5809.c-irene.mg1.tgcc.ccc.cea.fr_SEDNA_DELTA_MONITOR_02M_Fluxnet/
CPU times: user 3.69 s, sys: 732 ms, total: 4.42 s
Wall time: 1min 35s
Out[3]:

Client

Client-69c3f3da-13d3-11ed-8c05-080038b93b8b

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

584f656c

Dashboard: http://127.0.0.1:8787/status Workers: 64
Total threads: 256 Total memory: 251.06 GiB
Status: running Using processes: True

Scheduler Info

Scheduler

Scheduler-a0b54f86-accd-43c6-b616-8d127d07d403

Comm: tcp://127.0.0.1:41790 Workers: 64
Dashboard: http://127.0.0.1:8787/status Total threads: 256
Started: 1 minute ago Total memory: 251.06 GiB

Workers

Worker: 0
Comm: tcp://127.0.0.1:38508 Total threads: 4
Dashboard: http://127.0.0.1:39630/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42417
Local directory: /tmp/dask-worker-space/worker-q9tmv9sh

Worker: 1
Comm: tcp://127.0.0.1:40527 Total threads: 4
Dashboard: http://127.0.0.1:33802/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:43108
Local directory: /tmp/dask-worker-space/worker-isd5o9oy

Worker: 2
Comm: tcp://127.0.0.1:41061 Total threads: 4
Dashboard: http://127.0.0.1:38061/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36506
Local directory: /tmp/dask-worker-space/worker-it71h4i7

Worker: 3
Comm: tcp://127.0.0.1:43453 Total threads: 4
Dashboard: http://127.0.0.1:38681/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35404
Local directory: /tmp/dask-worker-space/worker-2osu2f0j

Worker: 4
Comm: tcp://127.0.0.1:46038 Total threads: 4
Dashboard: http://127.0.0.1:36937/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44829
Local directory: /tmp/dask-worker-space/worker-64w16l5r

Worker: 5
Comm: tcp://127.0.0.1:34884 Total threads: 4
Dashboard: http://127.0.0.1:33707/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36063
Local directory: /tmp/dask-worker-space/worker-jde57nzc

Worker: 6
Comm: tcp://127.0.0.1:46761 Total threads: 4
Dashboard: http://127.0.0.1:46471/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:39029
Local directory: /tmp/dask-worker-space/worker-0k7nzj7n

Worker: 7
Comm: tcp://127.0.0.1:42668 Total threads: 4
Dashboard: http://127.0.0.1:38036/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41607
Local directory: /tmp/dask-worker-space/worker-9iog2r2j

Worker: 8
Comm: tcp://127.0.0.1:44983 Total threads: 4
Dashboard: http://127.0.0.1:41627/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34682
Local directory: /tmp/dask-worker-space/worker-edxwuqi1

Worker: 9
Comm: tcp://127.0.0.1:42278 Total threads: 4
Dashboard: http://127.0.0.1:38415/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38354
Local directory: /tmp/dask-worker-space/worker-bj4ijeef

Worker: 10
Comm: tcp://127.0.0.1:38860 Total threads: 4
Dashboard: http://127.0.0.1:46442/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45093
Local directory: /tmp/dask-worker-space/worker-640690rg

Worker: 11
Comm: tcp://127.0.0.1:37685 Total threads: 4
Dashboard: http://127.0.0.1:38332/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35193
Local directory: /tmp/dask-worker-space/worker-ajvomah5

Worker: 12
Comm: tcp://127.0.0.1:35749 Total threads: 4
Dashboard: http://127.0.0.1:43137/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35897
Local directory: /tmp/dask-worker-space/worker-vagi3cal

Worker: 13
Comm: tcp://127.0.0.1:38540 Total threads: 4
Dashboard: http://127.0.0.1:38386/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42185
Local directory: /tmp/dask-worker-space/worker-j77uz_pp

Worker: 14
Comm: tcp://127.0.0.1:39313 Total threads: 4
Dashboard: http://127.0.0.1:37950/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45212
Local directory: /tmp/dask-worker-space/worker-yjz359v8

Worker: 15
Comm: tcp://127.0.0.1:45681 Total threads: 4
Dashboard: http://127.0.0.1:44969/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45448
Local directory: /tmp/dask-worker-space/worker-n4d7ti8h

Worker: 16
Comm: tcp://127.0.0.1:45486 Total threads: 4
Dashboard: http://127.0.0.1:39486/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46590
Local directory: /tmp/dask-worker-space/worker-4c9jj_sb

Worker: 17
Comm: tcp://127.0.0.1:33550 Total threads: 4
Dashboard: http://127.0.0.1:45019/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45502
Local directory: /tmp/dask-worker-space/worker-16o62jn7

Worker: 18
Comm: tcp://127.0.0.1:39274 Total threads: 4
Dashboard: http://127.0.0.1:35333/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37017
Local directory: /tmp/dask-worker-space/worker-jqxr5ekt

Worker: 19
Comm: tcp://127.0.0.1:35390 Total threads: 4
Dashboard: http://127.0.0.1:38552/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34038
Local directory: /tmp/dask-worker-space/worker-w5o8i_53

Worker: 20
Comm: tcp://127.0.0.1:44173 Total threads: 4
Dashboard: http://127.0.0.1:44882/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38099
Local directory: /tmp/dask-worker-space/worker-qjnx57ty

Worker: 21
Comm: tcp://127.0.0.1:42649 Total threads: 4
Dashboard: http://127.0.0.1:39651/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37911
Local directory: /tmp/dask-worker-space/worker-o8ba5cyd

Worker: 22
Comm: tcp://127.0.0.1:41003 Total threads: 4
Dashboard: http://127.0.0.1:46179/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38658
Local directory: /tmp/dask-worker-space/worker-74rn84xh

Worker: 23
Comm: tcp://127.0.0.1:41208 Total threads: 4
Dashboard: http://127.0.0.1:34830/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37376
Local directory: /tmp/dask-worker-space/worker-3w_qcfs0

Worker: 24
Comm: tcp://127.0.0.1:35145 Total threads: 4
Dashboard: http://127.0.0.1:37541/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36507
Local directory: /tmp/dask-worker-space/worker-iuu9ugyp

Worker: 25
Comm: tcp://127.0.0.1:45503 Total threads: 4
Dashboard: http://127.0.0.1:39944/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35483
Local directory: /tmp/dask-worker-space/worker-pt1lqild

Worker: 26
Comm: tcp://127.0.0.1:46782 Total threads: 4
Dashboard: http://127.0.0.1:32817/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38912
Local directory: /tmp/dask-worker-space/worker-a7tniwfl

Worker: 27
Comm: tcp://127.0.0.1:34673 Total threads: 4
Dashboard: http://127.0.0.1:45520/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37421
Local directory: /tmp/dask-worker-space/worker-yg5wult1

Worker: 28
Comm: tcp://127.0.0.1:40501 Total threads: 4
Dashboard: http://127.0.0.1:34328/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34425
Local directory: /tmp/dask-worker-space/worker-vumgbrtp

Worker: 29
Comm: tcp://127.0.0.1:33884 Total threads: 4
Dashboard: http://127.0.0.1:35225/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41888
Local directory: /tmp/dask-worker-space/worker-j4w0mash

Worker: 30
Comm: tcp://127.0.0.1:37423 Total threads: 4
Dashboard: http://127.0.0.1:42679/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42326
Local directory: /tmp/dask-worker-space/worker-ly7q5a8n

Worker: 31
Comm: tcp://127.0.0.1:44546 Total threads: 4
Dashboard: http://127.0.0.1:35334/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:34221
Local directory: /tmp/dask-worker-space/worker-mme_441g

Worker: 32
Comm: tcp://127.0.0.1:35548 Total threads: 4
Dashboard: http://127.0.0.1:46565/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46008
Local directory: /tmp/dask-worker-space/worker-005skei8

Worker: 33
Comm: tcp://127.0.0.1:37201 Total threads: 4
Dashboard: http://127.0.0.1:45936/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42744
Local directory: /tmp/dask-worker-space/worker-mxp68qg7

Worker: 34
Comm: tcp://127.0.0.1:34111 Total threads: 4
Dashboard: http://127.0.0.1:44412/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40578
Local directory: /tmp/dask-worker-space/worker-l4z9pkip

Worker: 35
Comm: tcp://127.0.0.1:45070 Total threads: 4
Dashboard: http://127.0.0.1:37210/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37010
Local directory: /tmp/dask-worker-space/worker-uk7iaikv

Worker: 36
Comm: tcp://127.0.0.1:45635 Total threads: 4
Dashboard: http://127.0.0.1:39492/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46832
Local directory: /tmp/dask-worker-space/worker-66vnzyv4

Worker: 37
Comm: tcp://127.0.0.1:45005 Total threads: 4
Dashboard: http://127.0.0.1:39579/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42005
Local directory: /tmp/dask-worker-space/worker-95jbz7x7

Worker: 38
Comm: tcp://127.0.0.1:40324 Total threads: 4
Dashboard: http://127.0.0.1:40002/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36992
Local directory: /tmp/dask-worker-space/worker-2f7wwvj_

Worker: 39
Comm: tcp://127.0.0.1:38300 Total threads: 4
Dashboard: http://127.0.0.1:34330/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44682
Local directory: /tmp/dask-worker-space/worker-d9k3l67y

Worker: 40
Comm: tcp://127.0.0.1:43266 Total threads: 4
Dashboard: http://127.0.0.1:36618/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35073
Local directory: /tmp/dask-worker-space/worker-tic2zljm

Worker: 41
Comm: tcp://127.0.0.1:40403 Total threads: 4
Dashboard: http://127.0.0.1:37322/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38669
Local directory: /tmp/dask-worker-space/worker-6lfslwy3

Worker: 42
Comm: tcp://127.0.0.1:38785 Total threads: 4
Dashboard: http://127.0.0.1:45543/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44045
Local directory: /tmp/dask-worker-space/worker-w0py11w3

Worker: 43
Comm: tcp://127.0.0.1:42225 Total threads: 4
Dashboard: http://127.0.0.1:41929/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:37684
Local directory: /tmp/dask-worker-space/worker-mr0z_duz

Worker: 44
Comm: tcp://127.0.0.1:41264 Total threads: 4
Dashboard: http://127.0.0.1:39858/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36433
Local directory: /tmp/dask-worker-space/worker-y397725g

Worker: 45
Comm: tcp://127.0.0.1:40479 Total threads: 4
Dashboard: http://127.0.0.1:40569/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38631
Local directory: /tmp/dask-worker-space/worker-0s6a20qj

Worker: 46
Comm: tcp://127.0.0.1:36854 Total threads: 4
Dashboard: http://127.0.0.1:38682/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42404
Local directory: /tmp/dask-worker-space/worker-q0vjkg79

Worker: 47
Comm: tcp://127.0.0.1:38286 Total threads: 4
Dashboard: http://127.0.0.1:45920/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33050
Local directory: /tmp/dask-worker-space/worker-vh60w9c6

Worker: 48
Comm: tcp://127.0.0.1:35643 Total threads: 4
Dashboard: http://127.0.0.1:40355/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:33094
Local directory: /tmp/dask-worker-space/worker-h2ew7mmc

Worker: 49
Comm: tcp://127.0.0.1:33026 Total threads: 4
Dashboard: http://127.0.0.1:42938/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:35880
Local directory: /tmp/dask-worker-space/worker-4rfg3q2r

Worker: 50
Comm: tcp://127.0.0.1:43611 Total threads: 4
Dashboard: http://127.0.0.1:43087/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45548
Local directory: /tmp/dask-worker-space/worker-5i_n0gtc

Worker: 51
Comm: tcp://127.0.0.1:35316 Total threads: 4
Dashboard: http://127.0.0.1:37021/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41852
Local directory: /tmp/dask-worker-space/worker-cr4yfn0g

Worker: 52
Comm: tcp://127.0.0.1:37743 Total threads: 4
Dashboard: http://127.0.0.1:43665/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41755
Local directory: /tmp/dask-worker-space/worker-q8n6pw3h

Worker: 53
Comm: tcp://127.0.0.1:40362 Total threads: 4
Dashboard: http://127.0.0.1:36474/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42072
Local directory: /tmp/dask-worker-space/worker-f6newwek

Worker: 54
Comm: tcp://127.0.0.1:33146 Total threads: 4
Dashboard: http://127.0.0.1:44108/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:39892
Local directory: /tmp/dask-worker-space/worker-o7bysmnv

Worker: 55
Comm: tcp://127.0.0.1:46713 Total threads: 4
Dashboard: http://127.0.0.1:36026/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:46126
Local directory: /tmp/dask-worker-space/worker-l3hy4fxr

Worker: 56
Comm: tcp://127.0.0.1:42144 Total threads: 4
Dashboard: http://127.0.0.1:45510/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:41074
Local directory: /tmp/dask-worker-space/worker-0lt99hjl

Worker: 57
Comm: tcp://127.0.0.1:36376 Total threads: 4
Dashboard: http://127.0.0.1:36476/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42356
Local directory: /tmp/dask-worker-space/worker-oi1eaoos

Worker: 58
Comm: tcp://127.0.0.1:43091 Total threads: 4
Dashboard: http://127.0.0.1:38102/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:44309
Local directory: /tmp/dask-worker-space/worker-tb7fx3zc

Worker: 59
Comm: tcp://127.0.0.1:43183 Total threads: 4
Dashboard: http://127.0.0.1:46359/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:40909
Local directory: /tmp/dask-worker-space/worker-ytlh550w

Worker: 60
Comm: tcp://127.0.0.1:38775 Total threads: 4
Dashboard: http://127.0.0.1:36487/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:36768
Local directory: /tmp/dask-worker-space/worker-svyz4tcq

Worker: 61
Comm: tcp://127.0.0.1:46768 Total threads: 4
Dashboard: http://127.0.0.1:37251/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:38945
Local directory: /tmp/dask-worker-space/worker-3mfw3yga

Worker: 62
Comm: tcp://127.0.0.1:41872 Total threads: 4
Dashboard: http://127.0.0.1:33902/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:42817
Local directory: /tmp/dask-worker-space/worker-kcwxs0qr

Worker: 63
Comm: tcp://127.0.0.1:41949 Total threads: 4
Dashboard: http://127.0.0.1:35023/status Memory: 3.92 GiB
Nanny: tcp://127.0.0.1:45219
Local directory: /tmp/dask-worker-space/worker-4gku5cak

read plotting information from a csv file¶

In [4]:
df=load.controlfile(control)
#Take out 'later' tagged computations
#df=df[~df['Value'].str.contains('later')]
df
Out[4]:
Value Inputs Equation Zone Plot Colourmap MinMax Unit Oldname Unnamed: 10
Fluxnet gridV.vomecrty,param.e3v_0,param.e1v,param.mas... calc.Fluxnet(data) FramS_All Fluxnet_integrals None ((-10,10),(-10,50) ,(-150,50),(-25,5) ) (Sv,TW, mSv,10^-2 Sv) I-6
Fluxnet gridV.vomecrty,param.e3v_0,param.e1v,param.mas... calc.Fluxnet(data) Davis Fluxnet_integrals None ((-5.0,5.0),(-25,27) ,(-200,50),(-9,5) ) (Sv,TW, mSv,10^-2 Sv) I-6
Fluxnet gridV.vomecrty,param.e3v_0,param.e1v,param.mas... calc.Fluxnet(data) Bering Fluxnet_integrals None ((-2,2),(-10,50) ,(-150,50),(-2,4) ) (Sv,TW, mSv,10^-2 Sv) I-6

Computation starts here¶

Each computation consists of

  1. Load NEMO data set
  2. Zoom data set
  3. Compute (or load computed data set)
  4. Save
  5. Plot
  6. Close
In [5]:
%%time
import os
calcswitch=os.environ.get('calc', 'True') 
lazy=os.environ.get('lazy','False' )
loaddata=((df.Inputs != '').any()) 
print('calcswitch=',calcswitch,'df.Inputs != nothing',loaddata, 'lazy=',lazy)
data = load.datas(catalog_url,df.Inputs,month,year,daskreport,lazy=lazy) if ((calcswitch=='True' )*loaddata) else 0 
data

calcswitch= False df.Inputs != nothing True lazy= False
CPU times: user 307 µs, sys: 53 µs, total: 360 µs
Wall time: 363 µs
Out[5]:
0
In [6]:
%%time
monitor.auto(df,data,savefig,daskreport,outputpath,file_exp='SEDNA'
            )

#calc= False
#save= False
#plot= True
Value='Fluxnet'
Zone='FramS_All'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-10, 10), (-10, 50), (-150, 50), (-25, 5))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_FramS_All_Fluxnet'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
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    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
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      long_name :
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      standard_name :
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      time_origin :
      1900-01-01 00:00:00
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 1, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(2608)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot02240: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02241: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02242: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02266: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02267: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02268: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot02290: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02291: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02292: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot02299: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_FramS_All_Fluxnet_20120101-20120228.html created

Value='Fluxnet'
Zone='Davis'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-5.0, 5.0), (-25, 27), (-200, 50), (-9, 5))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_Davis_Fluxnet'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_Davis_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
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    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
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      bounds :
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      long_name :
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      standard_name :
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      time_origin :
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      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
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      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
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      long_name :
      Time axis
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      time_origin :
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       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
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       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(1308)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot03176: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03177: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03178: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03202: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03203: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03204: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot03226: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03227: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03228: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot03235: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Davis_Fluxnet_20120101-20120228.html created

Value='Fluxnet'
Zone='Bering'
Plot='Fluxnet_integrals'
cmap='None'
clabel='(Sv,TW, mSv,10^-2 Sv)'
clim= ((-2, 2), (-10, 50), (-150, 50), (-2, 4))
outputpath='../results/SEDNA_DELTA_MONITOR/'
nc_outputpath='../nc_results/SEDNA_DELTA_MONITOR/'
filename='SEDNA_Fluxnet_integrals_Bering_Fluxnet'
#3 no computing , loading starts
data=save.load_data(plot=Plot,path=nc_outputpath,filename=filename)
start saving data
load 1Dnc file from ../nc_results/SEDNA_DELTA_MONITOR/../*/SEDNA_Fluxnet_integrals_Bering_Fluxnet*.nc
load computed data completed

<xarray.Dataset>
Dimensions:                (t: 90)
Coordinates:
    time_centered          (t) object dask.array<chunksize=(31,), meta=np.ndarray>
  * t                      (t) object 2012-01-01 12:00:00 ... 2012-01-31 12:0...
    y                      int64 ...
Data variables:
    Volume flux Net        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux Northward  (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Net          (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux Northward    (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Net         (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater Northward   (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Ice export             (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Volume flux South      (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Heat flux South        (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
    Freshwater South       (t) float64 dask.array<chunksize=(31,), meta=np.ndarray>
xarray.Dataset
    • t: 90
    • time_centered
      (t)
      object
      dask.array<chunksize=(31,), meta=np.ndarray>
      bounds :
      time_centered_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type object numpy.ndarray
      		90 1
    • t
      (t)
      object
      2012-01-01 12:00:00 ... 2012-01-...
      axis :
      T
      bounds :
      time_counter_bounds
      long_name :
      Time axis
      standard_name :
      time
      time_origin :
      1900-01-01 00:00:00
      array([cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 2, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 1, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 2, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 3, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 4, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 5, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 6, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 7, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 8, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 9, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 10, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 11, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 12, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 13, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 14, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 15, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 16, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 17, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 18, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 19, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 20, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 21, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 22, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 23, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 24, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 25, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 26, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 27, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 28, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 29, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 30, 12, 0, 0, 0, has_year_zero=True),
       cftime.DatetimeNoLeap(2012, 1, 31, 12, 0, 0, 0, has_year_zero=True)],
      dtype=object)
    • y
      ()
      int64
      ...
      array(6538)
    • Volume flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Net
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater Northward
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Ice export
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Volume flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Heat flux South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1
    • Freshwater South
      (t)
      float64
      dask.array<chunksize=(31,), meta=np.ndarray>
      Array Chunk
      Bytes 720 B 248 B
      Shape (90,) (31,)
      Count 9 Tasks 3 Chunks
      Type float64 numpy.ndarray
      		90 1 
#5 Plotting
filename= plots.Fluxnet_integrals(data,path=outputpath,filename=filename,save=savefig,cmap=cmap,clim=clim,clabel=clabel)

WARNING:param.CurvePlot04112: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04113: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04114: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04138: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04139: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04140: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html starts plotting

WARNING:param.CurvePlot04162: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04163: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04164: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.
WARNING:param.CurvePlot04171: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

plotting ../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html
../results/SEDNA_DELTA_MONITOR/SEDNA_Fluxnet_integrals_Bering_Fluxnet_20120101-20120228.html created

CPU times: user 15.4 s, sys: 6.46 s, total: 21.8 s
Wall time: 1min 8s